The New Science Intensive Standard of Master Preparation in Sphere of Competitive Intelligence on the Basis of Systemological Knowledge-Oriented Technologies

Methodological, theoretical and technological bases of the new branch standard of Ukraine higher education which regulates preparation process of masters - professionals in the information area and information analysts are considered. The new systemological knowledge-oriented technologies developed in KNURE which considerably surpass foreign analogues are put as the basis of training.

Stress, burnout and coping strategies in the emergency and intensive care hospital departments

Doctors and nurses working at the accident and emergency (A&E), and intensive care departments are at risk of burnout. They often spend substantial time in intense interactions with other people, centered on patients? health problems (physical, psychological and social) that may lead to feelings of anger, anxiety and frustration, and eventually to burnout. Burnout is a syndrome of emotional exhaustion, depersonalization and reduced personal accomplishment (Maslach & Jackson, 1981) The purpose of this chapter is to assess work stressors, burnout and stress-coping mechanisms among doctors and nurses at the A&E and intensive care departments. A quantitative design using the survey approach was used to collect data from a sample of 200 participants with a response rate of 71% (n=154) Work stressors were associated with burnout in both doctors and nurses. Workload was the most salient work stressor in the sample. Nurses experienced more stress (M=1.5, SD=0.4) than doctors (M=1.2, SD=0.4) in all the work stressor variables examined. The A&E department was reported as more stressful than the intensive care department. Avoidance-oriented and task-oriented coping were the most and the least frequently reported coping strategies respectively. Additionally, only emotion-oriented coping strategy was significantly different between doctors and nurses, and this strategy was also significantly positively correlated with all the variables in the adapted nursing stress scale, and the three burnout variables. Death and dying was most strongly correlated with emotion-oriented coping. This chapter provides an assessment of stress, burnout and coping experienced by both doctors and nurses within the A&E and intensive care departments. Methods that may mitigate stress in these environments may be adequate staffing, supportive management, stress management programs, as well as improvement in communication strategies between doctors and nurses.

Lack of effect of lowering LDL cholesterol on cancer: meta-analysis of individual data from 175,000 people in 27 randomised trials of statin therapy

Background: Statin therapy reduces the risk of occlusive vascular events, but uncertainty remains about potential effects on cancer. We sought to provide a detailed assessment of any effects on cancer of lowering LDL cholesterol (LDL-C) with a statin using individual patient records from 175,000 patients in 27 large-scale statin trials. Methods and Findings: Individual records of 134,537 participants in 22 randomised trials of statin versus control (median duration 4.8 years) and 39,612 participants in 5 trials of more intensive versus less intensive statin therapy (median duration 5.1 years) were obtained. Reducing LDL-C with a statin for about 5 years had no effect on newly diagnosed cancer or on death from such cancers in either the trials of statin versus control (cancer incidence: 3755 [1.4% per year [py]] versus 3738 [1.4% py], RR 1.00 [95% CI 0.96-1.05]; cancer mortality: 1365 [0.5% py] versus 1358 [0.5% py], RR 1.00 [95% CI 0.93-1.08]) or in the trials of more versus less statin (cancer incidence: 1466 [1.6% py] vs 1472 [1.6% py], RR 1.00 [95% CI 0.93-1.07]; cancer mortality: 447 [0.5% py] versus 481 [0.5% py], RR 0.93 [95% CI 0.82-1.06]). Moreover, there was no evidence of any effect of reducing LDL-C with statin therapy on cancer incidence or mortality at any of 23 individual categories of sites, with increasing years of treatment, for any individual statin, or in any given subgroup. In particular, among individuals with low baseline LDL-C (<2 mmol/L), there was no evidence that further LDL-C reduction (from about 1.7 to 1.3 mmol/L) increased cancer risk (381 [1.6% py] versus 408 [1.7% py]; RR 0.92 [99% CI 0.76-1.10]). Conclusions: In 27 randomised trials, a median of five years of statin therapy had no effect on the incidence of, or mortality from, any type of cancer (or the aggregate of all cancer).

Extending Science Gateway Frameworks to Support Big Data Applications in the Cloud

Cloud computing offers massive scalability and elasticity required by many scien-tific and commercial applications. Combining the computational and data handling capabilities of clouds with parallel processing also has the potential to tackle Big Data problems efficiently. Science gateway frameworks and workflow systems enable application developers to implement complex applications and make these available for end-users via simple graphical user interfaces. The integration of such frameworks with Big Data processing tools on the cloud opens new oppor-tunities for application developers. This paper investigates how workflow sys-tems and science gateways can be extended with Big Data processing capabilities. A generic approach based on infrastructure aware workflows is suggested and a proof of concept is implemented based on the WS-PGRADE/gUSE science gateway framework and its integration with the Hadoop parallel data processing solution based on the MapReduce paradigm in the cloud. The provided analysis demonstrates that the methods described to integrate Big Data processing with workflows and science gateways work well in different cloud infrastructures and application scenarios, and can be used to create massively parallel applications for scientific analysis of Big Data.

Data Science Seminar: Generic Big Data Processing for Advancing Situation Awareness and Decision-Support

The generation of heterogeneous big data sources with ever increasing volumes, velocities and veracities over the he last few years has inspired the data science and research community to address the challenge of extracting knowledge form big data. Such a wealth of generated data across the board can be intelligently exploited to advance our knowledge about our environment, public health, critical infrastructure and security. In recent years we have developed generic approaches to process such big data at multiple levels for advancing decision-support. It specifically concerns data processing with semantic harmonisation, low level fusion, analytics, knowledge modelling with high level fusion and reasoning. Such approaches will be introduced and presented in context of the TRIDEC project results on critical oil and gas industry drilling operations and also the ongoing large eVacuate project on critical crowd behaviour detection in confined spaces.

Data Science Seminar: The data science revolution in Physics and Astronomy

Abstract Heading into the 2020s, Physics and Astronomy are undergoing experimental revolutions that will reshape our picture of the fabric of the Universe. The Large Hadron Collider (LHC), the largest particle physics project in the world, produces 30 petabytes of data annually that need to be sifted through, analysed, and modelled. In astrophysics, the Large Synoptic Survey Telescope (LSST) will be taking a high-resolution image of the full sky every 3 days, leading to data rates of 30 terabytes per night over ten years. These experiments endeavour to answer the question why 96% of the content of the universe currently elude our physical understanding. Both the LHC and LSST share the 5-dimensional nature of their data, with position, energy and time being the fundamental axes. This talk will present an overview of the experiments and data that is gathered, and outlines the challenges in extracting information. Common strategies employed are very similar to industrial data! Science problems (e.g., data filtering, machine learning, statistical interpretation) and provide a seed for exchange of knowledge between academia and industry. Speaker Biography Professor Mark Sullivan Mark Sullivan is a Professor of Astrophysics in the Department of Physics and Astronomy. Mark completed his PhD at Cambridge, and following postdoctoral study in Durham, Toronto and Oxford, now leads a research group at Southampton studying dark energy using exploding stars called "type Ia supernovae". Mark has many years' experience of research that involves repeatedly imaging the night sky to track the arrival of transient objects, involving significant challenges in data handling, processing, classification and analysis.

KE & Science Europe briefing paper: Funding Research Data Management and related infrastructures

Responsible Research Data Management (RDM) is a pillar of quality research. In practice good RDM requires the support of a well-functioning Research Data Infrastructure (RDI). One of the challenges the research community is facing is how to fund the management of research data and the required infrastructure. Knowledge Exchange and Science Europe have both defined activities to explore how RDM/RDI are, or can be, funded. Independently they each planned to survey users and providers of data services and on becoming aware of the similar objectives and approaches, the Science Europe Working Group on Research Data and the Knowledge Exchange Research Data expert group joined forces and devised a joint activity to to inform the discussion on the funding of RDM/RDI in Europe.

DATA DRIVEN APPROACHES TO IDENTIFY DETERMINANTS OF HEART DISEASES AND CANCER RESISTANCE

Cancer and cardio-vascular diseases are the leading causes of death world-wide. Caused by systemic genetic and molecular disruptions in cells, these disorders are the manifestation of profound disturbance of normal cellular homeostasis. People suffering or at high risk for these disorders need early diagnosis and personalized therapeutic intervention. Successful implementation of such clinical measures can significantly improve global health. However, development of effective therapies is hindered by the challenges in identifying genetic and molecular determinants of the onset of diseases; and in cases where therapies already exist, the main challenge is to identify molecular determinants that drive resistance to the therapies. Due to the progress in sequencing technologies, the access to a large genome-wide biological data is now extended far beyond few experimental labs to the global research community. The unprecedented availability of the data has revolutionized the capabilities of computational researchers, enabling them to collaboratively address the long standing problems from many different perspectives. Likewise, this thesis tackles the two main public health related challenges using data driven approaches. Numerous association studies have been proposed to identify genomic variants that determine disease. However, their clinical utility remains limited due to their inability to distinguish causal variants from associated variants. In the presented thesis, we first propose a simple scheme that improves association studies in supervised fashion and has shown its applicability in identifying genomic regulatory variants associated with hypertension. Next, we propose a coupled Bayesian regression approach -- eQTeL, which leverages epigenetic data to estimate regulatory and gene interaction potential, and identifies combinations of regulatory genomic variants that explain the gene expression variance. On human heart data, eQTeL not only explains a significantly greater proportion of expression variance in samples, but also predicts gene expression more accurately than other methods. We demonstrate that eQTeL accurately detects causal regulatory SNPs by simulation, particularly those with small effect sizes. Using various functional data, we show that SNPs detected by eQTeL are enriched for allele-specific protein binding and histone modifications, which potentially disrupt binding of core cardiac transcription factors and are spatially proximal to their target. eQTeL SNPs capture a substantial proportion of genetic determinants of expression variance and we estimate that 58% of these SNPs are putatively causal. The challenge of identifying molecular determinants of cancer resistance so far could only be dealt with labor intensive and costly experimental studies, and in case of experimental drugs such studies are infeasible. Here we take a fundamentally different data driven approach to understand the evolving landscape of emerging resistance. We introduce a novel class of genetic interactions termed synthetic rescues (SR) in cancer, which denotes a functional interaction between two genes where a change in the activity of one vulnerable gene (which may be a target of a cancer drug) is lethal, but subsequently altered activity of its partner rescuer gene restores cell viability. Next we describe a comprehensive computational framework --termed INCISOR-- for identifying SR underlying cancer resistance. Applying INCISOR to mine The Cancer Genome Atlas (TCGA), a large collection of cancer patient data, we identified the first pan-cancer SR networks, composed of interactions common to many cancer types. We experimentally test and validate a subset of these interactions involving the master regulator gene mTOR. We find that rescuer genes become increasingly activated as breast cancer progresses, testifying to pervasive ongoing rescue processes. We show that SRs can be utilized to successfully predict patients' survival and response to the majority of current cancer drugs, and importantly, for predicting the emergence of drug resistance from the initial tumor biopsy. Our analysis suggests a potential new strategy for enhancing the effectiveness of existing cancer therapies by targeting their rescuer genes to counteract resistance. The thesis provides statistical frameworks that can harness ever increasing high throughput genomic data to address challenges in determining the molecular underpinnings of hypertension, cardiovascular disease and cancer resistance. We discover novel molecular mechanistic insights that will advance the progress in early disease prevention and personalized therapeutics. Our analyses sheds light on the fundamental biological understanding of gene regulation and interaction, and opens up exciting avenues of translational applications in risk prediction and therapeutics.

Aplicación de técnicas de Big Data Science para la gestión de crisis

A pesar de la existencia de una multitud de investigaciones sobre el análisis de sentimiento, existen pocos trabajos que traten el tema de su implantación práctica y real y su integración con la inteligencia de negocio y big data de tal forma que dichos análisis de sentimiento estén incorporados en una arquitectura (que soporte todo el proceso desde la obtención de datos hasta su explotación con las herramientas de BI) aplicada a la gestión de la crisis. Se busca, por medio de este trabajo, investigar cómo se pueden unir los mundos de análisis (de sentimiento y crisis) y de la tecnología (todo lo relacionado con la inteligencia de negocios, minería de datos y Big Data), y crear una solución de Inteligencia de Negocios que comprenda la minería de datos y el análisis de sentimiento (basados en grandes volúmenes de datos), y que ayude a empresas y/o gobiernos con la gestión de crisis. El autor se ha puesto a estudiar formas de trabajar con grandes volúmenes de datos, lo que se conoce actualmente como Big Data Science, o la ciencia de los datos aplicada a grandes volúmenes de datos (Big Data), y unir esta tecnología con el análisis de sentimiento relacionado a una situación real (en este trabajo la situación elegida fue la del proceso de impechment de la presidenta de Brasil, Dilma Rousseff). En esta unión se han utilizado técnicas de inteligencia de negocios para la creación de cuadros de mandos, rutinas de ETC (Extracción, Transformación y Carga) de los datos así como también técnicas de minería de textos y análisis de sentimiento. El trabajo ha sido desarrollado en distintas partes y con distintas fuentes de datos (datasets) debido a las distintas pruebas de tecnología a lo largo del proyecto. Uno de los datasets más importantes del proyecto son los tweets recogidos entre los meses de diciembre de 2015 y enero de 2016. Los mensajes recogidos contenían la palabra "Dilma" en el mensaje. Todos los twittees fueron recogidos con la API de Streaming del Twitter. Es muy importante entender que lo que se publica en la red social Twitter no se puede manipular y representa la opinión de la persona o entidad que publica el mensaje. Por esto se puede decir que hacer el proceso de minería de datos con los datos del Twitter puede ser muy eficiente y verídico. En 3 de diciembre de 2015 se aceptó la petición de apertura del proceso del impechment del presidente de Brasil, Dilma Rousseff. La petición fue aceptada por el presidente de la Cámara de los Diputados, el diputado Sr. Eduardo Cunha (PMDBRJ), y de este modo se creó una expectativa sobre el sentimiento de la población y el futuro de Brasil. También se ha recogido datos de las búsquedas en Google referentes a la palabra Dilma; basado en estos datos, el objetivo es llegar a un análisis global de sentimiento (no solo basado en los twittees recogidos). Utilizando apenas dos fuentes (Twitter y búsquedas de Google) han sido extraídos muchísimos datos, pero hay muchas otras fuentes donde es posible obtener informaciones con respecto de las opiniones de las personas acerca de un tema en particular. Así, una herramienta que pueda recoger, extraer y almacenar tantos datos e ilustrar las informaciones de una manera eficaz que ayude y soporte una toma de decisión, contribuye para la gestión de crisis.

Using multivariate sequential patterns to improve survival prediction in intensive care burn unit

Resuscitation and stabilization are key issues in Intensive Care Burn Units and early survival predictions help to decide the best clinical action during these phases. Current survival scores of burns focus on clinical variables such as age or the body surface area. However, the evolution of other parameters (e.g. diuresis or fluid balance) during the first days is also valuable knowledge. In this work we suggest a methodology and we propose a Temporal Data Mining algorithm to estimate the survival condition from the patient’s evolution. Experiments conducted on 480 patients show the improvement of survival prediction.

Data science

Guest lecture COMP1205, fundamentals and applications of data science

Estimation of water use and crop coefficients for an intensive olive orchard using sap flow measurements and modeled data

Olive tree sap flow measurements were collected in an intensive orchard near Évora, Portugal, during the irrigation seasons of 2013 and 2014, to calculate daily tree transpiration rates (T_SF). Meteorological variables were also collected to calculate reference evapotranspiration (ETo). Both data were used to assess values of basal crop coefficient (Kcb) for the period of the sap flow observations. The soil water balance model SIMDualKc was calibrated with soil, biophysical ground data and sap flow measurements collected in 2013. Validated in 2014 with collected sap flow observations, the model was used to provide estimates of dual e single crop coefficients for 2014 crop growing season. Good agreement between model simulated daily transpiration rates and those obtained with sapflow measurements was observed for 2014 (R2=0.76, RMSE=0.20 mm d-1), the year of validation, with an estimation average absolute error (AAE) of 0.20 mm d-1. Olive modeled daily actual evapotranspiration resulted in atual ETc values of 0.87, 2.05 and 0.77 mm d-1 for 2014 initial, mid- and end-season, respectively. Actual crop coefficient (Kc act) values of 0.51, 0.43 and 0.67 were also obtained for the same periods, respectively. Higher Kc values during spring (initial stage) and autumn (end-stage) were published in FAO56, varying between 0.65 for Kc ini and 0.70 for Kc end. The lower Kc mid value of 0.43 obtained for the summer (mid-season) is also inconsistent with the FAO56 expected Kc mid value of 0.70 for the period. The modeled Kc results are more consistent with the ones published by Allen & Pereira [1] for olive orchards with effective ground cover of 0.25 to 0.5, which vary between 0.40 and 0.80 for Kc ini, 0.40–0.60 for Kc mid with no active ground cover, and 0.35–0.75 for Kc end, depending on ground cover. The SIMDualKc simulation model proved to be appropriate for obtaining evapotranspiration and crop coefficient values for our intensive olive orchard in southern Portugal.

On the role of Computational Logic in Data Science: representing, learning, reasoning, and explaining knowledge

In this thesis we discuss in what ways computational logic (CL) and data science (DS) can jointly contribute to the management of knowledge within the scope of modern and future artificial intelligence (AI), and how technically-sound software technologies can be realised along the path. An agent-oriented mindset permeates the whole discussion, by stressing pivotal role of autonomous agents in exploiting both means to reach higher degrees of intelligence. Accordingly, the goals of this thesis are manifold. First, we elicit the analogies and differences among CL and DS, hence looking for possible synergies and complementarities along 4 major knowledge-related dimensions, namely representation, acquisition (a.k.a. learning), inference (a.k.a. reasoning), and explanation. In this regard, we propose a conceptual framework through which bridges these disciplines can be described and designed. We then survey the current state of the art of AI technologies, w.r.t. their capability to support bridging CL and DS in practice. After detecting lacks and opportunities, we propose the notion of logic ecosystem as the new conceptual, architectural, and technological solution supporting the incremental integration of symbolic and sub-symbolic AI. Finally, we discuss how our notion of logic ecosys- tem can be reified into actual software technology and extended towards many DS-related directions.

The European legal approach to Open Science and research data

This dissertation proposes an analysis of the governance of the European scientific research, focusing on the emergence of the Open Science paradigm: a new way of doing science, oriented towards the openness of every phase of the scientific research process, able to take full advantage of the digital ICTs. The emergence of this paradigm is relatively recent, but in the last years it has become increasingly relevant. The European institutions expressed a clear intention to embrace the Open Science paradigm (eg., think about the European Open Science Cloud, EOSC; or the establishment of the Horizon Europe programme). This dissertation provides a conceptual framework for the multiple interventions of the European institutions in the field of Open Science, addressing the major legal challenges of its implementation. The study investigates the notion of Open Science, proposing a definition that takes into account all its dimensions related to the human and fundamental rights framework in which Open Science is grounded. The inquiry addresses the legal challenges related to the openness of research data, in light of the European Open Data framework and the impact of the GDPR on the context of Open Science. The last part of the study is devoted to the infrastructural dimension of the Open Science paradigm, exploring the e-infrastructures. The focus is on a specific type of computational infrastructure: the High Performance Computing (HPC) facility. The adoption of HPC for research is analysed from the European perspective, investigating the EuroHPC project, and the local perspective, proposing the case study of the HPC facility of the University of Luxembourg, the ULHPC. This dissertation intends to underline the relevance of the legal coordination approach, between all actors and phases of the process, in order to develop and implement the Open Science paradigm, adhering to the underlying human and fundamental rights.